README.md

LingFeat - Comprehensive Linguistic Features Extraction Tool for Readability Assessment and Text Simplification

• LingFeat - Comprehensive Linguistic Features Extraction Tool for Readability Assessment and Text Simplification
  • Overview
  • Things to note
  • Citation
  • Installation
  • Usage
  • Available Features, Code, Definition
  • Key References
  • License
  • Acknowledgement

Migration Notice - 2023-03-06

LingFeat is now maintained in a new repository named LFTK. The new library will have more focus on usability, coverage, multilingualism, and expandability.

Upgrade Notice - 2022-10-18

I am currently updating this repository, including project structure, feature coverage, and etc. The already existing issues will be reflected, too. Please email at brucelws@seas.upenn.edu for any suggestions. Thank you community for the patience.

Overview

LingFeat is a Python research package for various handcrafted linguistic features. More specifically, LingFeat is an NLP feature extraction software, which currently extracts 255 linguistic features from English string input.

These features can be divided into five broad linguistic branches:

1. Advanced Semantic (AdSem): for measuing complexity of meaning structures (Not working in some cases. Working on this issue.)

   • Semantic Richness, Noise, and Clarity from trained LDA models (included, no training required)

2. Discourse (Disco): for measuring coherence/cohesion

   • Entity Counts, Entity Grid, and Local Coherence score

3. Syntactic (Synta): for measuring the complexity of grammar and structure

   • Phrasal Counts (e.g. Noun Phrase), Part-of-Speech Counts, and Tree Structure

4. Lexico Semantic (LxSem): for measuring word/phrasal-specific difficulty

   • Type Token Ratio, Variation Score (e.g. Verb Variation), Age-of-Acquistion, and SubtlexUS Frequency

5. Shallow Traditional (ShTra): traditional features/formulas for text difficulty

   • Basic Average Counts (words per sentence), Flesch-Kincaid Reading Ease, Smog, Gunning Fog, ...

Things to note

LingFeat is mainly built for text complexity/difficulty/readability analysis or text simplification studies. But it's role is to simply extract numerical linguistic faetures from a text. Hence, the use cases may vary.

We provide guidelines for both basic users and advanced users. Please follow Usage section.

Citation

This software is built for our paper on

@inproceedings{lee-etal-2021-pushing,
title = "Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features",
author = "Lee, Bruce W. and Jang, Yoo Sung and Lee, Jason",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.834" doi = "10.18653/v1/2021.emnlp-main.834",
pages = "10669--10686"}

Please cite our paper and provide link to this repository if you use in this software in research.

Most supported features are developed for passage analysis. One sentence input will work fine with the program but won't produce reliable output.

Installation

Option 1. Use package manager pip to install LingFeat.

pip install lingfeat

Option 2. Install from the repo. (Recommended)

You'll need to install the dependencies, including spaCy, by yourself. Ideally, use virtual environment (optional).

Use code below for option 2.

git clone https://github.com/brucewlee/lingfeat.git
pip install -r lingfeat/requirements.txt

Usage

A. General Purpose (basic)

If you aren't deeply interested in linguistics, you usually don't require the full feature set of LingFeat.

The following code returns a dictionary of 6 outputs from commonly used formulas in predicting readability:

• Flesch Kincaid Grade Level (Feature Code: FleschG_S)
• Automated Readability Index (Feature Code: AutoRea_S)
• Coleman Liau Readability Score (Feature Code: ColeLia_S)
• Smog Index (Feature Code: SmogInd_S)
• Gunning Fog Count Score (Feature Code: Gunning_S)
• Linsear Write Formula Score (Feature Code: LinseaW_S)

These formulas are a little outdated... but still widely used.

They are designed to match U.S. grade level from 1~12 (i.e. average student of the grade can read the text).

Ideally, you could average these 6 outputs to obtain a reliable outcome.

from lingfeat import extractor

text = "..."

LingFeat = extractor.pass_text(text)

LingFeat.preprocess()

TraF = LingFeat.TraF_()

print(TraF)

B. Research/ML/NLP Purpose (advanced)

B.1 Spacy Requirements

This library assumes that you have spaCy sm corpus (that is compatible with spaCy 3.0+) installed. If not, or if you aren't sure, run the following in terminal.

python -m spacy download en_core_web_sm

B.2. Example

Due to the wide number of supported features, we defined subgroups for features. Hence, features are not accessible individually. Instead, you'd call the subgroups to obtain the dictionary of the corresponding features.

To broadly understand how these features interact with text readability, difficulty, and complexity, I highly suggest you read Section 2 and 3 in our EMNLP paper.

"""
Import

this is the only import you need
"""
from lingfeat import extractor


"""
Pass text

here, text must be in string type
"""
text = "..."
LingFeat = extractor.pass_text(text)


"""
Preprocess text

options (all boolean):
- short (default False): include short words of < 3 letters
- see_token (default False): return token list
- see_sent_token (default False): return tokens in sentences

output:
- n_token
- n_sent
- token_list (optional)
- sent_token_list (optional)
"""
LingFeat.preprocess()
# or
# print(LingFeat.preprocess())


"""
Extract features

each method returns a dictionary of the corresponding features
"""
# Advanced Semantic (AdSem) Features
WoKF = LingFeat.WoKF_() # Wikipedia Knowledge Features
WBKF = LingFeat.WBKF_() # WeeBit Corpus Knowledge Features
OSKF = LingFeat.OSKF_() # OneStopEng Corpus Knowledge Features

# Discourse (Disco) Features
EnDF = LingFeat.EnDF_() # Entity Density Features
EnGF = LingFeat.EnGF_() # Entity Grid Features

# Syntactic (Synta) Features
PhrF = LingFeat.PhrF_() # Noun/Verb/Adj/Adv/... Phrasal Features
TrSF = LingFeat.TrSF_() # (Parse) Tree Structural Features
POSF = LingFeat.POSF_() # Noun/Verb/Adj/Adv/... Part-of-Speech Features

# Lexico Semantic (LxSem) Features
TTRF = LingFeat.TTRF_() # Type Token Ratio Features
VarF = LingFeat.VarF_() # Noun/Verb/Adj/Adv Variation Features 
PsyF = LingFeat.PsyF_() # Psycholinguistic Difficulty of Words (AoA Kuperman)
WoLF = LingFeat.WorF_() # Word Familiarity from Frequency Count (SubtlexUS)

# Shallow Traditional (ShTra) Features
ShaF = LingFeat.ShaF_() # Shallow Features (e.g. avg number of tokens)
TraF = LingFeat.TraF_() # Traditional Formulas 

Available Features, Code, Definition

idx	Linguistic Branch	Subgroup Code	Subgroup Definition	Feature Code	Feature Definition
1	AdSem	WoKF_	Wiki Knowledge Features	WRich05_S	Semantic Richness, 50 topics extracted from Wikipedia
2	AdSem	WoKF_	Wiki Knowledge Features	WClar05_S	Semantic Clarity, 50 topics extracted from Wikipedia
3	AdSem	WoKF_	Wiki Knowledge Features	WNois05_S	Semantic Noise, 50 topics extracted from Wikipedia
4	AdSem	WoKF_	Wiki Knowledge Features	WTopc05_S	Number of topics, 50 topics extracted from Wikipedia
5	AdSem	WoKF_	Wiki Knowledge Features	WRich10_S	Semantic Richness, 100 topics extracted from Wikipedia
6	AdSem	WoKF_	Wiki Knowledge Features	WClar10_S	Semantic Clarity, 100 topics extracted from Wikipedia
7	AdSem	WoKF_	Wiki Knowledge Features	WNois10_S	Semantic Noise, 100 topics extracted from Wikipedia
8	AdSem	WoKF_	Wiki Knowledge Features	WTopc10_S	Number of topics, 100 topics extracted from Wikipedia
9	AdSem	WoKF_	Wiki Knowledge Features	WRich15_S	Semantic Richness, 150 topics extracted from Wikipedia
10	AdSem	WoKF_	Wiki Knowledge Features	WClar15_S	Semantic Clarity, 150 topics extracted from Wikipedia
11	AdSem	WoKF_	Wiki Knowledge Features	WNois15_S	Semantic Noise, 150 topics extracted from Wikipedia
12	AdSem	WoKF_	Wiki Knowledge Features	WTopc15_S	Number of topics, 150 topics extracted from Wikipedia
13	AdSem	WoKF_	Wiki Knowledge Features	WRich20_S	Semantic Richness, 200 topics extracted from Wikipedia
14	AdSem	WoKF_	Wiki Knowledge Features	WClar20_S	Semantic Clarity, 200 topics extracted from Wikipedia
15	AdSem	WoKF_	Wiki Knowledge Features	WNois20_S	Semantic Noise, 200 topics extracted from Wikipedia
16	AdSem	WoKF_	Wiki Knowledge Features	WTopc20_S	Number of topics, 200 topics extracted from Wikipedia
17	AdSem	WBKF_	WB Knowledge Features	BRich05_S	Semantic Richness, 50 topics extracted from WeeBit Corpus
18	AdSem	WBKF_	WB Knowledge Features	BClar05_S	Semantic Clarity, 50 topics extracted from WeeBit Corpus
19	AdSem	WBKF_	WB Knowledge Features	BNois05_S	Semantic Noise, 50 topics extracted from WeeBit Corpus
20	AdSem	WBKF_	WB Knowledge Features	BTopc05_S	Number of topics, 50 topics extracted from WeeBit Corpus
21	AdSem	WBKF_	WB Knowledge Features	BRich10_S	Semantic Richness, 100 topics extracted from WeeBit Corpus
22	AdSem	WBKF_	WB Knowledge Features	BClar10_S	Semantic Clarity, 100 topics extracted from WeeBit Corpus
23	AdSem	WBKF_	WB Knowledge Features	BNois10_S	Semantic Noise, 100 topics extracted from WeeBit Corpus
24	AdSem	WBKF_	WB Knowledge Features	BTopc10_S	Number of topics, 100 topics extracted from WeeBit Corpus
25	AdSem	WBKF_	WB Knowledge Features	BRich15_S	Semantic Richness, 150 topics extracted from WeeBit Corpus
26	AdSem	WBKF_	WB Knowledge Features	BClar15_S	Semantic Clarity, 150 topics extracted from WeeBit Corpus
27	AdSem	WBKF_	WB Knowledge Features	BNois15_S	Semantic Noise, 150 topics extracted from WeeBit Corpus
28	AdSem	WBKF_	WB Knowledge Features	BTopc15_S	Number of topics, 150 topics extracted from WeeBit Corpus
29	AdSem	WBKF_	WB Knowledge Features	BRich20_S	Semantic Richness, 200 topics extracted from WeeBit Corpus
30	AdSem	WBKF_	WB Knowledge Features	BClar20_S	Semantic Clarity, 200 topics extracted from WeeBit Corpus
31	AdSem	WBKF_	WB Knowledge Features	BNois20_S	Semantic Noise, 200 topics extracted from WeeBit Corpus
32	AdSem	WBKF_	WB Knowledge Features	BTopc20_S	Number of topics, 200 topics extracted from WeeBit Corpus
33	AdSem	OSKF_	OSE Knowledge Features	ORich05_S	Semantic Richness, 50 topics extracted from OneStopEng Corpus
34	AdSem	OSKF_	OSE Knowledge Features	OClar05_S	Semantic Clarity, 50 topics extracted from OneStopEng Corpus
35	AdSem	OSKF_	OSE Knowledge Features	ONois05_S	Semantic Noise, 50 topics extracted from OneStopEng Corpus
36	AdSem	OSKF_	OSE Knowledge Features	OTopc05_S	Number of topics, 50 topics extracted from OneStopEng Corpus
37	AdSem	OSKF_	OSE Knowledge Features	ORich10_S	Semantic Richness, 100 topics extracted from OneStopEng Corpus
38	AdSem	OSKF_	OSE Knowledge Features	OClar10_S	Semantic Clarity, 100 topics extracted from OneStopEng Corpus
39	AdSem	OSKF_	OSE Knowledge Features	ONois10_S	Semantic Noise, 100 topics extracted from OneStopEng Corpus
40	AdSem	OSKF_	OSE Knowledge Features	OTopc10_S	Number of topics, 100 topics extracted from OneStopEng Corpus
41	AdSem	OSKF_	OSE Knowledge Features	ORich15_S	Semantic Richness, 150 topics extracted from OneStopEng Corpus
42	AdSem	OSKF_	OSE Knowledge Features	OClar15_S	Semantic Clarity, 150 topics extracted from OneStopEng Corpus
43	AdSem	OSKF_	OSE Knowledge Features	ONois15_S	Semantic Noise, 150 topics extracted from OneStopEng Corpus
44	AdSem	OSKF_	OSE Knowledge Features	OTopc15_S	Number of topics, 150 topics extracted from OneStopEng Corpus
45	AdSem	OSKF_	OSE Knowledge Features	ORich20_S	Semantic Richness, 200 topics extracted from OneStopEng Corpus
46	AdSem	OSKF_	OSE Knowledge Features	OClar20_S	Semantic Clarity, 200 topics extracted from OneStopEng Corpus
47	AdSem	OSKF_	OSE Knowledge Features	ONois20_S	Semantic Noise, 200 topics extracted from OneStopEng Corpus
48	AdSem	OSKF_	OSE Knowledge Features	OTopc20_S	Number of topics, 200 topics extracted from OneStopEng Corpus
49	Disco	EnDF_	Entity Density Features	to_EntiM_C	total number of Entities Mentions counts
50	Disco	EnDF_	Entity Density Features	as_EntiM_C	average number of Entities Mentions counts per sentence
51	Disco	EnDF_	Entity Density Features	at_EntiM_C	average number of Entities Mentions counts per token (word)
52	Disco	EnDF_	Entity Density Features	to_UEnti_C	total number of unique Entities
53	Disco	EnDF_	Entity Density Features	as_UEnti_C	average number of unique Entities per sentence
54	Disco	EnDF_	Entity Density Features	at_UEnti_C	average number of unique Entities per token (word)
55	Disco	EnGF_	Entity Grid Features	ra_SSToT_C	ratio of ss transitions to total
56	Disco	EnGF_	Entity Grid Features	ra_SOToT_C	ratio of so transitions to total
57	Disco	EnGF_	Entity Grid Features	ra_SXToT_C	ratio of sx transitions to total
58	Disco	EnGF_	Entity Grid Features	ra_SNToT_C	ratio of sn transitions to total
59	Disco	EnGF_	Entity Grid Features	ra_OSToT_C	ratio of os transitions to total
60	Disco	EnGF_	Entity Grid Features	ra_OOToT_C	ratio of oo transitions to total
61	Disco	EnGF_	Entity Grid Features	ra_OXToT_C	ratio of ox transitions to total
62	Disco	EnGF_	Entity Grid Features	ra_ONToT_C	ratio of on transitions to total
63	Disco	EnGF_	Entity Grid Features	ra_XSToT_C	ratio of xs transitions to total
64	Disco	EnGF_	Entity Grid Features	ra_XOToT_C	ratio of xo transitions to total
65	Disco	EnGF_	Entity Grid Features	ra_XXToT_C	ratio of xx transitions to total
66	Disco	EnGF_	Entity Grid Features	ra_XNToT_C	ratio of xn transitions to total
67	Disco	EnGF_	Entity Grid Features	ra_NSToT_C	ratio of ns transitions to total
68	Disco	EnGF_	Entity Grid Features	ra_NOToT_C	ratio of no transitions to total
69	Disco	EnGF_	Entity Grid Features	ra_NXToT_C	ratio of nx transitions to total
70	Disco	EnGF_	Entity Grid Features	ra_NNToT_C	ratio of nn transitions to total
71	Disco	EnGF_	Entity Grid Features	LoCohPA_S	Local Coherence for PA score
72	Disco	EnGF_	Entity Grid Features	LoCohPW_S	Local Coherence for PW score
73	Disco	EnGF_	Entity Grid Features	LoCohPU_S	Local Coherence for PU score
74	Disco	EnGF_	Entity Grid Features	LoCoDPA_S	Local Coherence distance for PA score
75	Disco	EnGF_	Entity Grid Features	LoCoDPW_S	Local Coherence distance for PW score
76	Disco	EnGF_	Entity Grid Features	LoCoDPU_S	Local Coherence distance for PU score
77	Synta	PhrF_	Phrasal Features	to_NoPhr_C	total count of Noun phrases
78	Synta	PhrF_	Phrasal Features	as_NoPhr_C	average count of Noun phrases per sentence
79	Synta	PhrF_	Phrasal Features	at_NoPhr_C	average count of Noun phrases per token
80	Synta	PhrF_	Phrasal Features	ra_NoVeP_C	ratio of Noun phrases count to Verb phrases count
81	Synta	PhrF_	Phrasal Features	ra_NoSuP_C	ratio of Noun phrases count to Subordinate Clauses count
82	Synta	PhrF_	Phrasal Features	ra_NoPrP_C	ratio of Noun phrases count to Prep phrases count
83	Synta	PhrF_	Phrasal Features	ra_NoAjP_C	ratio of Noun phrases count to Adj phrases count
84	Synta	PhrF_	Phrasal Features	ra_NoAvP_C	ratio of Noun phrases count to Adv phrases count
85	Synta	PhrF_	Phrasal Features	to_VePhr_C	total count of Verb phrases
86	Synta	PhrF_	Phrasal Features	as_VePhr_C	average count of Verb phrases per sentence
87	Synta	PhrF_	Phrasal Features	at_VePhr_C	average count of Verb phrases per token
88	Synta	PhrF_	Phrasal Features	ra_VeNoP_C	ratio of Verb phrases count to Noun phrases count
89	Synta	PhrF_	Phrasal Features	ra_VeSuP_C	ratio of Verb phrases count to Subordinate Clauses count
90	Synta	PhrF_	Phrasal Features	ra_VePrP_C	ratio of Verb phrases count to Prep phrases count
91	Synta	PhrF_	Phrasal Features	ra_VeAjP_C	ratio of Verb phrases count to Adj phrases count
92	Synta	PhrF_	Phrasal Features	ra_VeAvP_C	ratio of Verb phrases count to Adv phrases count
93	Synta	PhrF_	Phrasal Features	to_SuPhr_C	total count of Subordinate Clauses
94	Synta	PhrF_	Phrasal Features	as_SuPhr_C	average count of Subordinate Clauses per sentence
95	Synta	PhrF_	Phrasal Features	at_SuPhr_C	average count of Subordinate Clauses per token
96	Synta	PhrF_	Phrasal Features	ra_SuNoP_C	ratio of Subordinate Clauses count to Noun phrases count
97	Synta	PhrF_	Phrasal Features	ra_SuVeP_C	ratio of Subordinate Clauses count to Verb phrases count
98	Synta	PhrF_	Phrasal Features	ra_SuPrP_C	ratio of Subordinate Clauses count to Prep phrases count
99	Synta	PhrF_	Phrasal Features	ra_SuAjP_C	ratio of Subordinate Clauses count to Adj phrases count
100	Synta	PhrF_	Phrasal Features	ra_SuAvP_C	ratio of Subordinate Clauses count to Adv phrases count
101	Synta	PhrF_	Phrasal Features	to_PrPhr_C	total count of prepositional phrases
102	Synta	PhrF_	Phrasal Features	as_PrPhr_C	average count of prepositional phrases per sentence
103	Synta	PhrF_	Phrasal Features	at_PrPhr_C	average count of prepositional phrases per token
104	Synta	PhrF_	Phrasal Features	ra_PrNoP_C	ratio of Prep phrases count to Noun phrases count
105	Synta	PhrF_	Phrasal Features	ra_PrVeP_C	ratio of Prep phrases count to Verb phrases count
106	Synta	PhrF_	Phrasal Features	ra_PrSuP_C	ratio of Prep phrases count to Subordinate Clauses count
107	Synta	PhrF_	Phrasal Features	ra_PrAjP_C	ratio of Prep phrases count to Adj phrases count
108	Synta	PhrF_	Phrasal Features	ra_PrAvP_C	ratio of Prep phrases count to Adv phrases count
109	Synta	PhrF_	Phrasal Features	to_AjPhr_C	total count of Adjective phrases
110	Synta	PhrF_	Phrasal Features	as_AjPhr_C	average count of Adjective phrases per sentence
111	Synta	PhrF_	Phrasal Features	at_AjPhr_C	average count of Adjective phrases per token
112	Synta	PhrF_	Phrasal Features	ra_AjNoP_C	ratio of Adj phrases count to Noun phrases count
113	Synta	PhrF_	Phrasal Features	ra_AjVeP_C	ratio of Adj phrases count to Verb phrases count
114	Synta	PhrF_	Phrasal Features	ra_AjSuP_C	ratio of Adj phrases count to Subordinate Clauses count
115	Synta	PhrF_	Phrasal Features	ra_AjPrP_C	ratio of Adj phrases count to Prep phrases count
116	Synta	PhrF_	Phrasal Features	ra_AjAvP_C	ratio of Adj phrases count to Adv phrases count
117	Synta	PhrF_	Phrasal Features	to_AvPhr_C	total count of Adverb phrases
118	Synta	PhrF_	Phrasal Features	as_AvPhr_C	average count of Adverb phrases per sentence
119	Synta	PhrF_	Phrasal Features	at_AvPhr_C	average count of Adverb phrases per token
120	Synta	PhrF_	Phrasal Features	ra_AvNoP_C	ratio of Adv phrases count to Noun phrases count
121	Synta	PhrF_	Phrasal Features	ra_AvVeP_C	ratio of Adv phrases count to Verb phrases count
122	Synta	PhrF_	Phrasal Features	ra_AvSuP_C	ratio of Adv phrases count to Subordinate Clauses count
123	Synta	PhrF_	Phrasal Features	ra_AvPrP_C	ratio of Adv phrases count to Prep phrases count
124	Synta	PhrF_	Phrasal Features	ra_AvAjP_C	ratio of Adv phrases count to Adj phrases count
125	Synta	TrSF_	Tree Structure Features	to_TreeH_C	total Tree height of all sentences
126	Synta	TrSF_	Tree Structure Features	as_TreeH_C	average Tree height per sentence
127	Synta	TrSF_	Tree Structure Features	at_TreeH_C	average Tree height per token (word)
128	Synta	TrSF_	Tree Structure Features	to_FTree_C	total length of flattened Trees
129	Synta	TrSF_	Tree Structure Features	as_FTree_C	average length of flattened Trees per sentence
130	Synta	TrSF_	Tree Structure Features	at_FTree_C	average length of flattened Trees per token (word)
131	Synta	POSF_	Part-of-Speech Features	to_NoTag_C	total count of Noun POS tags
132	Synta	POSF_	Part-of-Speech Features	as_NoTag_C	average count of Noun POS tags per sentence
133	Synta	POSF_	Part-of-Speech Features	at_NoTag_C	average count of Noun POS tags per token
134	Synta	POSF_	Part-of-Speech Features	ra_NoAjT_C	ratio of Noun POS count to Adjective POS count
135	Synta	POSF_	Part-of-Speech Features	ra_NoVeT_C	ratio of Noun POS count to Verb POS count
136	Synta	POSF_	Part-of-Speech Features	ra_NoAvT_C	ratio of Noun POS count to Adverb POS count
137	Synta	POSF_	Part-of-Speech Features	ra_NoSuT_C	ratio of Noun POS count to Subordinating Conjunction count
138	Synta	POSF_	Part-of-Speech Features	ra_NoCoT_C	ratio of Noun POS count to Coordinating Conjunction count
139	Synta	POSF_	Part-of-Speech Features	to_VeTag_C	total count of Verb POS tags
140	Synta	POSF_	Part-of-Speech Features	as_VeTag_C	average count of Verb POS tags per sentence
141	Synta	POSF_	Part-of-Speech Features	at_VeTag_C	average count of Verb POS tags per token
142	Synta	POSF_	Part-of-Speech Features	ra_VeAjT_C	ratio of Verb POS count to Adjective POS count
143	Synta	POSF_	Part-of-Speech Features	ra_VeNoT_C	ratio of Verb POS count to Noun POS count
144	Synta	POSF_	Part-of-Speech Features	ra_VeAvT_C	ratio of Verb POS count to Adverb POS count
145	Synta	POSF_	Part-of-Speech Features	ra_VeSuT_C	ratio of Verb POS count to Subordinating Conjunction count
146	Synta	POSF_	Part-of-Speech Features	ra_VeCoT_C	ratio of Verb POS count to Coordinating Conjunction count
147	Synta	POSF_	Part-of-Speech Features	to_AjTag_C	total count of Adjective POS tags
148	Synta	POSF_	Part-of-Speech Features	as_AjTag_C	average count of Adjective POS tags per sentence
149	Synta	POSF_	Part-of-Speech Features	at_AjTag_C	average count of Adjective POS tags per token
150	Synta	POSF_	Part-of-Speech Features	ra_AjNoT_C	ratio of Adjective POS count to Noun POS count
151	Synta	POSF_	Part-of-Speech Features	ra_AjVeT_C	ratio of Adjective POS count to Verb POS count
152	Synta	POSF_	Part-of-Speech Features	ra_AjAvT_C	ratio of Adjective POS count to Adverb POS count
153	Synta	POSF_	Part-of-Speech Features	ra_AjSuT_C	ratio of Adjective POS count to Subordinating Conjunction count
154	Synta	POSF_	Part-of-Speech Features	ra_AjCoT_C	ratio of Adjective POS count to Coordinating Conjunction count
155	Synta	POSF_	Part-of-Speech Features	to_AvTag_C	total count of Adverb POS tags
156	Synta	POSF_	Part-of-Speech Features	as_AvTag_C	average count of Adverb POS tags per sentence
157	Synta	POSF_	Part-of-Speech Features	at_AvTag_C	average count of Adverb POS tags per token
158	Synta	POSF_	Part-of-Speech Features	ra_AvAjT_C	ratio of Adverb POS count to Adjective POS count
159	Synta	POSF_	Part-of-Speech Features	ra_AvNoT_C	ratio of Adverb POS count to Noun POS count
160	Synta	POSF_	Part-of-Speech Features	ra_AvVeT_C	ratio of Adverb POS count to Verb POS count
161	Synta	POSF_	Part-of-Speech Features	ra_AvSuT_C	ratio of Adverb POS count to Subordinating Conjunction count
162	Synta	POSF_	Part-of-Speech Features	ra_AvCoT_C	ratio of Adverb POS count to Coordinating Conjunction count
163	Synta	POSF_	Part-of-Speech Features	to_SuTag_C	total count of Subordinating Conjunction POS tags
164	Synta	POSF_	Part-of-Speech Features	as_SuTag_C	average count of Subordinating Conjunction POS tags per sentence
165	Synta	POSF_	Part-of-Speech Features	at_SuTag_C	average count of Subordinating Conjunction POS tags per token
166	Synta	POSF_	Part-of-Speech Features	ra_SuAjT_C	ratio of Subordinating Conjunction POS count to Adjective POS count
167	Synta	POSF_	Part-of-Speech Features	ra_SuNoT_C	ratio of Subordinating Conjunction POS count to Noun POS count
168	Synta	POSF_	Part-of-Speech Features	ra_SuVeT_C	ratio of Subordinating Conjunction POS count to Verb POS count
169	Synta	POSF_	Part-of-Speech Features	ra_SuAvT_C	ratio of Subordinating Conjunction POS count to Adverb POS count
170	Synta	POSF_	Part-of-Speech Features	ra_SuCoT_C	ratio of Subordinating Conjunction POS count to Coordinating Conjunction count
171	Synta	POSF_	Part-of-Speech Features	to_CoTag_C	total count of Coordinating Conjunction POS tags
172	Synta	POSF_	Part-of-Speech Features	as_CoTag_C	average count of Coordinating Conjunction POS tags per sentence
173	Synta	POSF_	Part-of-Speech Features	at_CoTag_C	average count of Coordinating Conjunction POS tags per token
174	Synta	POSF_	Part-of-Speech Features	ra_CoAjT_C	ratio of Coordinating Conjunction POS count to Adjective POS count
175	Synta	POSF_	Part-of-Speech Features	ra_CoNoT_C	ratio of Coordinating Conjunction POS count to Noun POS count
176	Synta	POSF_	Part-of-Speech Features	ra_CoVeT_C	ratio of Coordinating Conjunction POS count to Verb POS count
177	Synta	POSF_	Part-of-Speech Features	ra_CoAvT_C	ratio of Coordinating Conjunction POS count to Adverb POS count
178	Synta	POSF_	Part-of-Speech Features	ra_CoSuT_C	ratio of Coordinating Conjunction POS count to Subordinating Conjunction count
179	Synta	POSF_	Part-of-Speech Features	to_ContW_C	total count of Content words
180	Synta	POSF_	Part-of-Speech Features	as_ContW_C	average count of Content words per sentence
181	Synta	POSF_	Part-of-Speech Features	at_ContW_C	average count of Content words per token
182	Synta	POSF_	Part-of-Speech Features	to_FuncW_C	total count of Function words
183	Synta	POSF_	Part-of-Speech Features	as_FuncW_C	average count of Function words per sentence
184	Synta	POSF_	Part-of-Speech Features	at_FuncW_C	average count of Function words per token
185	Synta	POSF_	Part-of-Speech Features	ra_CoFuW_C	ratio of Content words to Function words
186	LxSem	VarF_	Variation Ratio Features	SimpNoV_S	unique Nouns/total Nouns (Noun Variation-1)
187	LxSem	VarF_	Variation Ratio Features	SquaNoV_S	(unique Nouns**2)/total Nouns (Squared Noun Variation-1)
188	LxSem	VarF_	Variation Ratio Features	CorrNoV_S	unique Nouns/sqrt(2*total Nouns) (Corrected Noun Variation-1)
189	LxSem	VarF_	Variation Ratio Features	SimpVeV_S	unique Verbs/total Verbs (Verb Variation-1)
190	LxSem	VarF_	Variation Ratio Features	SquaVeV_S	(unique Verbs**2)/total Verbs (Squared Verb Variation-1)
191	LxSem	VarF_	Variation Ratio Features	CorrVeV_S	unique Verbs/sqrt(2*total Verbs) (Corrected Verb Variation-1)
192	LxSem	VarF_	Variation Ratio Features	SimpAjV_S	unique Adjectives/total Adjectives (Adjective Variation-1)
193	LxSem	VarF_	Variation Ratio Features	SquaAjV_S	(unique Adjectives**2)/total Adjectives (Squared Adjective Variation-1)
194	LxSem	VarF_	Variation Ratio Features	CorrAjV_S	unique Adjectives/sqrt(2*total Adjectives) (Corrected Adjective Variation-1)
195	LxSem	VarF_	Variation Ratio Features	SimpAvV_S	unique Adverbs/total Adverbs (AdVerb Variation-1)
196	LxSem	VarF_	Variation Ratio Features	SquaAvV_S	(unique Adverbs**2)/total Adverbs (Squared AdVerb Variation-1)
197	LxSem	VarF_	Variation Ratio Features	CorrAvV_S	unique Adverbs/sqrt(2*total Adverbs) (Corrected AdVerb Variation-1)
198	LxSem	TTRF_	Type Token Ratio Features	SimpTTR_S	unique tokens/total tokens (TTR)
199	LxSem	TTRF_	Type Token Ratio Features	CorrTTR_S	unique tokens/sqrt(2*total tokens) (Corrected TTR)
200	LxSem	TTRF_	Type Token Ratio Features	BiLoTTR_S	log(unique tokens)/log(total tokens) (Bi-Logarithmic TTR)
201	LxSem	TTRF_	Type Token Ratio Features	UberTTR_S	(log(unique tokens))^2/log(total tokens/unique tokens) (Uber Index)
202	LxSem	TTRF_	Type Token Ratio Features	MTLDTTR_S	Measure of Textual Lexical Diversity (default TTR = 0.72)
203	LxSem	PsyF_	Psycholinguistic Features	to_AAKuW_C	total AoA (Age of Acquisition) of words
204	LxSem	PsyF_	Psycholinguistic Features	as_AAKuW_C	average AoA of words per sentence
205	LxSem	PsyF_	Psycholinguistic Features	at_AAKuW_C	average AoA of words per token
206	LxSem	PsyF_	Psycholinguistic Features	to_AAKuL_C	total lemmas AoA of lemmas
207	LxSem	PsyF_	Psycholinguistic Features	as_AAKuL_C	average lemmas AoA of lemmas per sentence
208	LxSem	PsyF_	Psycholinguistic Features	at_AAKuL_C	average lemmas AoA of lemmas per token
209	LxSem	PsyF_	Psycholinguistic Features	to_AABiL_C	total lemmas AoA of lemmas, Bird norm
210	LxSem	PsyF_	Psycholinguistic Features	as_AABiL_C	average lemmas AoA of lemmas, Bird norm per sentence
211	LxSem	PsyF_	Psycholinguistic Features	at_AABiL_C	average lemmas AoA of lemmas, Bird norm per token
212	LxSem	PsyF_	Psycholinguistic Features	to_AABrL_C	total lemmas AoA of lemmas, Bristol norm
213	LxSem	PsyF_	Psycholinguistic Features	as_AABrL_C	average lemmas AoA of lemmas, Bristol norm per sentence
214	LxSem	PsyF_	Psycholinguistic Features	at_AABrL_C	average lemmas AoA of lemmas, Bristol norm per token
215	LxSem	PsyF_	Psycholinguistic Features	to_AACoL_C	total AoA of lemmas, Cortese and Khanna norm
216	LxSem	PsyF_	Psycholinguistic Features	as_AACoL_C	average AoA of lemmas, Cortese and Khanna norm per sentence
217	LxSem	PsyF_	Psycholinguistic Features	at_AACoL_C	average AoA of lemmas, Cortese and Khanna norm per token
218	LxSem	WorF_	Word Familiarity	to_SbFrQ_C	total SubtlexUS FREQcount value
219	LxSem	WorF_	Word Familiarity	as_SbFrQ_C	average SubtlexUS FREQcount value per sentenc
220	LxSem	WorF_	Word Familiarity	at_SbFrQ_C	average SubtlexUS FREQcount value per token
221	LxSem	WorF_	Word Familiarity	to_SbCDC_C	total SubtlexUS CDcount value
222	LxSem	WorF_	Word Familiarity	as_SbCDC_C	average SubtlexUS CDcount value per sentence
223	LxSem	WorF_	Word Familiarity	at_SbCDC_C	average SubtlexUS CDcount value per token
224	LxSem	WorF_	Word Familiarity	to_SbFrL_C	total SubtlexUS FREQlow value
225	LxSem	WorF_	Word Familiarity	as_SbFrL_C	average SubtlexUS FREQlow value per sentence
226	LxSem	WorF_	Word Familiarity	at_SbFrL_C	average SubtlexUS FREQlow value per token
227	LxSem	WorF_	Word Familiarity	to_SbCDL_C	total SubtlexUS CDlow value
228	LxSem	WorF_	Word Familiarity	as_SbCDL_C	average SubtlexUS CDlow value per sentence
229	LxSem	WorF_	Word Familiarity	at_SbCDL_C	average SubtlexUS CDlow value per token
230	LxSem	WorF_	Word Familiarity	to_SbSBW_C	total SubtlexUS SUBTLWF value
231	LxSem	WorF_	Word Familiarity	as_SbSBW_C	average SubtlexUS SUBTLWF value per sentence
232	LxSem	WorF_	Word Familiarity	at_SbSBW_C	average SubtlexUS SUBTLWF value per token
233	LxSem	WorF_	Word Familiarity	to_SbL1W_C	total SubtlexUS Lg10WF value
234	LxSem	WorF_	Word Familiarity	as_SbL1W_C	average SubtlexUS Lg10WF value per sentence
235	LxSem	WorF_	Word Familiarity	at_SbL1W_C	average SubtlexUS Lg10WF value per token
236	LxSem	WorF_	Word Familiarity	to_SbSBC_C	total SubtlexUS SUBTLCD value
237	LxSem	WorF_	Word Familiarity	as_SbSBC_C	average SubtlexUS SUBTLCD value per sentence
238	LxSem	WorF_	Word Familiarity	at_SbSBC_C	average SubtlexUS SUBTLCD value per token
239	LxSem	WorF_	Word Familiarity	to_SbL1C_C	total SubtlexUS Lg10CD value
240	LxSem	WorF_	Word Familiarity	as_SbL1C_C	average SubtlexUS Lg10CD value per sentence
241	LxSem	WorF_	Word Familiarity	at_SbL1C_C	average SubtlexUS Lg10CD value per token
242	ShaTr	ShaF_	Shallow Features	TokSenM_S	total count of tokens x total count of sentence
243	ShaTr	ShaF_	Shallow Features	TokSenS_S	sqrt(total count of tokens x total count of sentence)
244	ShaTr	ShaF_	Shallow Features	TokSenL_S	log(total count of tokens)/log(total count of sentence)
245	ShaTr	ShaF_	Shallow Features	as_Token_C	average count of tokens per sentence
246	ShaTr	ShaF_	Shallow Features	as_Sylla_C	average count of syllables per sentence
247	ShaTr	ShaF_	Shallow Features	at_Sylla_C	average count of syllables per token
248	ShaTr	ShaF_	Shallow Features	as_Chara_C	average count of characters per sentence
249	ShaTr	ShaF_	Shallow Features	at_Chara_C	average count of characters per token
250	ShaTr	TraF_	Traditional Formulas	SmogInd_S	Smog Index
251	ShaTr	TraF_	Traditional Formulas	ColeLia_S	Coleman Liau Readability Score
252	ShaTr	TraF_	Traditional Formulas	Gunning_S	Gunning Fog Count Score
253	ShaTr	TraF_	Traditional Formulas	AutoRea_S	New Automated Readability Index
254	ShaTr	TraF_	Traditional Formulas	FleschG_S	Flesch Kincaid Grade Level
255	ShaTr	TraF_	Traditional Formulas	LinseaW_S	Linsear Write Formula Score

Key References

We list only the key references here. Check our paper for full references.

Entity Density Features

Feng, Lijun, Noémie Elhadad, and Matt Huenerfauth. "Cognitively motivated features for readability assessment." Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009). 2009.

Entity Grid Features

Palma, Diego, and John Atkinson. "Coherence-based automatic essay assessment." IEEE Intelligent Systems 33.5 (2018): 26-36. Repository: https://github.com/dpalmasan/TRUNAJOD2.0

Variation Features

Lu, Xiaofei. "A corpus‐based evaluation of syntactic complexity measures as indices of college‐level ESL writers' language development." TESOL quarterly 45.1 (2011): 36-62.

Psycholinguistic Features

Kuperman, Victor, Hans Stadthagen-Gonzalez, and Marc Brysbaert. "Age-of-acquisition ratings for 30,000 English words." Behavior research methods 44.4 (2012): 978-990.

Traditional Formulas

Kincaid, J. Peter, Robert P. Fishburne Jr, Richard L. Rogers, and Brad S. Chissom. Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel. Naval Technical Training Command Millington TN Research Branch, 1975.

License

We license LingFeat source code under Creative Commons Attribution Share Alike 4.0 license (CC-BY-SA-4.0).

Under CC-BY-SA-4.0 license, you are allowed to distribute, modify, or privately use this repository.

But patent use, trademark use, and warranty use are not permitted. Research, code, or etc. that builds on top of LingFeat must be released under the same license. Alternatively, a similar open source license may be used.

Acknowledgement

I made this software during my time at LXPER AI, Seoul, South Korea. We decided to make this software openly available strictly for academic research purpose only.

I thank the firm and all other members of the R&D department for making this research possible.